A Case study of Edutainment Robot: Applying Voice Question Answering to Intelligent Robot

Oh, Hyo-Jung; Lee, Chung-Hee; Hwang, Yi-Gyu; Jang, Myung-Gil; Park, Jeon Gue; Lee, Yun Kun

doi:10.1109/roman.2007.4415119

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…with children for the purpose of education [5], [6] or edutainment (education + entertainment) [7]. Tielman et al [8] proposed a robot that adaptively expresses various emotions by using its voice and gestures.…”

Section: Red Greenmentioning

confidence: 99%

“…The quiz game is one of the most interesting forms of multiparty interaction and the robot quizmaster is a good application of speech-based interaction techniques [6], [7], [8], [14], [15], [16], [17], [18], [19]. Required tasks of a quizmaster are 1) managing the progress of a quiz game and 2) livening up the players and spectators.…”

Section: Multiparty Interaction In Quiz Gamementioning

confidence: 99%

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Development of a robot quizmaster with auditory functions for speech-based multiparty interaction

Nishimuta

Yoshii

Itoyama

et al. 2014

2014 IEEE/SICE International Symposium on System Integration

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Abstract-This paper presents a robot quizmaster that has auditory functions (i.e., ears) for moderating a multiplayer quiz game. The most basic form of oral interaction in a quiz game is that a quizmaster reads aloud a question, and each player is allowed to answer it whenever the answer comes to his or her mind. A critical problem in such oral interaction is that if multiple players speak almost simultaneously for answering, it is difficult for a "human" quizmaster to recognize overlapping answers and judge the correctness of each answer. To avoid this problem, players have conventionally been required to push a button, raise a hand, or say "Yes" to just get a right to answer a question before doing it. This requirement, however, inhibits natural oral interaction. In this paper we propose a "robot" quizmaster that can identify a player who correctly answers a question first, even when multiple players utter answers almost at the same time. Since our robot uses its own microphones (ears) embedded in the head, individual players are not required to wear small pin microphones close to their mouths. To localize, separate, and recognize overlapping utterances captured by the ears, we use a robot audition software called HARK and an automatic speech recognizer called Julius. Experimental results showed the effectiveness of our approach.

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Section: Red Greenmentioning

confidence: 99%

Section: Multiparty Interaction In Quiz Gamementioning

confidence: 99%

Development of a robot quizmaster with auditory functions for speech-based multiparty interaction

Nishimuta

Yoshii

Itoyama

et al. 2014

2014 IEEE/SICE International Symposium on System Integration

View full text Add to dashboard Cite

show abstract

“…Several robots become our state of the arts of this research: Sophia, the social Humanoid Robot and rst Citizen Robot in the world, developed by Hanson Robotics [4], Edutainment Robot, the close domain question answering robot, with answer base that constructed from the encyclopedia, developed by Oh HJ et al [5], Hospital Receptionist Robot, developed by Ahn HS et al [6], that can express emotion, friendliness and can recognize the face. DialogFlow used by the Hospital Receptionist Robot for the conversation decision-making method.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

Budiharto

Andreas

Gunawan

2020

Preprint

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Background- The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings- The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions- Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM / F1 score than using the CNN encoder.

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“…AI-guided chatbots are a specific application of AI that integrates natural language understanding, machine learning, knowledge base, and other related technologies (Gang et al, 2014; Gunawan et al, 2018; Oh et al, 2007). The chatbots in this article are mainly based on textual knowledge and use natural language processing to understand the user’s questions and then give the corresponding answers rather than being based on vocal instructions (like Apple’s Siri).…”

mentioning

confidence: 99%

Understanding the Determinants in the Different Government AI Adoption Stages: Evidence of Local Government Chatbots in China

Youkui

Zhang

Zhao

2020

Social Science Computer Review

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With the rapid development of artificial intelligence (AI), countries are increasingly adopting AI-guided chatbots to improve service on government portals. The reduction in face-to-face services under COVID-19 pandemic will further accelerate this trend. However, the adoption and performance of the existing chatbots differ. Based on the literature on e-government adoption and innovative policy innovation diffusion, this article examines both the initial and postadoption stages of chatbot usage in China’s local governments. While the first phase employs the survival model of event history analysis to explore the factors that influence the decision to adopt chatbots in local government, the second phase analyzes the determinants of those chatbots’ performance in the postadoption stage. We find that pressure factors and readiness factors play different roles in the different adoption stages. Although pressure can encourage local governments to implement chatbots, these governments’ readiness determined how well the chatbots perform after their initial adoption. The implications and limitations of the research are also discussed.

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A Case study of Edutainment Robot: Applying Voice Question Answering to Intelligent Robot

Cited by 6 publications

References 6 publications

Development of a robot quizmaster with auditory functions for speech-based multiparty interaction

Development of a robot quizmaster with auditory functions for speech-based multiparty interaction

Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

Understanding the Determinants in the Different Government AI Adoption Stages: Evidence of Local Government Chatbots in China

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